Satellite Image Data
Analysis using numpy
Data Source: Satellite Image from WIFIRE Project
Name: Md. Abrar Jahin¶
Roll:1811035¶
Department: Industrial Engineering and Management¶
Batch: 2018¶
University: Khulna University of Engineering and Technology, Khulna¶
Submission Date: 01 May 2020¶
WIFIRE is an integrated system for wildfire analysis, with specific regard to changing urban dynamics and climate. The system integrates networked observations such as heterogeneous satellite data and real-time remote sensor data, with computational techniques in signal processing, visualization, modeling, and data assimilation to provide a scalable method to monitor such phenomena as weather patterns that can help predict a wildfire's rate of spread. The details about WIFIRE are mentioned at: https://wifire.ucsd.edu/¶
In this project, I analyzed a sample satellite image dataset from WIFIRE using the numpy Library.
Loading the libraries we need: numpy, scipy, matplotlib
%matplotlib inline
import numpy as np
from scipy import misc
import matplotlib.pyplot as plt
import imageio
Creating a numpy array from an image file:
Let's choose a WIFIRE satellite image file as an ndarray and display its type.
from skimage import data
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
type(photo_data)
Now let's see what is in this image.
plt.figure(figsize=(15,15))
plt.imshow (photo_data)
photo_data.shape
#print(photo_data)
The shape of the ndarray show that it is a three layered matrix. The first two numbers here are length and width, and the third number (i.e. 3) is for three layers: Red, Green and Blue.
RGB Color Mapping in the Photo:
RED pixel indicates Altitude
BLUE pixel indicates Aspect
GREEN pixel indicates Slope
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The higher values denote higher altitude, aspect and slope.
photo_data.size
photo_data.min(), photo_data.max()
photo_data.mean()
Pixel on the 150th Row and 250th Column
photo_data[150, 250]#150=r,250=col,17, 35, 255=rgb
photo_data[150, 250, 1]#0,1,2 means r,g,b
Set a Pixel to All Zeros
We can set all three layer in a pixel as once by assigning zero globally to that (row,column) pairing. However, setting one pixel to zero is not noticeable.
#photo_data = misc.imread('./wifire/sd-3layers.jpg')
photo_data[150, 250] = 0
plt.figure(figsize=(10,10))
plt.imshow(photo_data)
Changing colors in a Range
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
photo_data[200:800, : ,1] = 255
plt.figure(figsize=(10,10))
plt.imshow(photo_data)
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
photo_data[200:800, :] = 255
plt.figure(figsize=(10,10))
plt.imshow(photo_data)
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
photo_data[200:800, :] = 0
plt.figure(figsize=(10,10))
plt.imshow(photo_data)
Pick all Pixels with Low Values
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
print("Shape of photo_data:", photo_data.shape)
low_value_filter = photo_data < 200
print("Shape of low_value_filter:", low_value_filter.shape)
Filtering Out Low Values
import random
plt.figure(figsize=(10,10))
plt.imshow(photo_data)
photo_data[low_value_filter] = 0
plt.figure(figsize=(10,10))
plt.imshow(photo_data)
More Row and Column Operations
rows_range = np.arange(len(photo_data))
cols_range = rows_range
print(type(rows_range))
print(rows_range)
photo_data[rows_range, cols_range] = 255
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
Masking Images
total_rows, total_cols, total_layers = photo_data.shape
print("photo_data = ", photo_data.shape)
X, Y = np.ogrid[:total_rows, :total_cols]
print("X = ", X.shape, " and Y = ", Y.shape)
print(Y)
center_row, center_col = total_rows / 2, total_cols / 2
print("center_row = ", center_row, "AND center_col = ", center_col)
print(X - center_row)
print(Y - center_col)
dist_from_center = (X - center_row)**2 + (Y - center_col)**2
print(dist_from_center)
radius = (total_rows / 2)**2
print("Radius = ", radius)
circular_mask = (dist_from_center > radius)
print(circular_mask)
print(circular_mask[1500:1700,2000:2200])
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
photo_data[circular_mask] = 0
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
Further Masking
Half Upper Masking
import numpy as np
X, Y = np.ogrid[:total_rows, :total_cols]
half_upper = X < center_row # this line generates a mask for all rows above the center
half_upper_mask = np.logical_and(half_upper, circular_mask)
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
photo_data[half_upper_mask] = 255
photo_data[half_upper_mask] = random.randint(200,255)
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
Further Processing of our Satellite Imagery
Processing of RED Pixels
We know, red pixels tell us about the height. I highlighted all the high altitude areas. I did this by detecting high intensity RED Pixels and muting down other areas.
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
red_mask = photo_data[:, : ,0] < 150
photo_data[red_mask] = 0
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
Detecting High-GREEN Pixels
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
green_mask = photo_data[:, : ,1] < 150
photo_data[green_mask] = 0
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
Detecting Highly-BLUE Pixels
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
blue_mask = photo_data[:, : ,2] < 150
photo_data[blue_mask] = 0
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
Composite mask that takes thresholds on all three layers: RED, GREEN, BLUE
photo_data = imageio.imread('./wifire/sd-3layers.jpg')
red_mask = photo_data[:, : ,0] < 150
green_mask = photo_data[:, : ,1] > 100
blue_mask = photo_data[:, : ,2] < 100
final_mask = np.logical_and(red_mask, green_mask, blue_mask)
photo_data[final_mask] = 0
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
